What are the key anthropogenic or land-related factors which most contribute to freshwater fish threatenedness globally?
IUCN Red List (12,452 freshwater fish species, 2,678 threatened) species) (IUCN, n.d.)
HydroSHEDS catchment boundaries - level 6 resolution (16,024) (Lehner and Grill 2013)
Hydrosheds downloaded from https://www.hydrosheds.org/products/hydrobasins and combined with the dataset to one Shapefile.
Reading layer `Hydrosheds_all' from data source
`/Users/justinecarey/Library/Mobile Documents/com~apple~CloudDocs/09_study/BOKU/2023-2024 WS/Exploratory Data Analysis in R/Final/Hydrosheds_all.shp'
using driver `ESRI Shapefile'
Simple feature collection with 16397 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -55.9875 xmax: 180 ymax: 83.62564
Geodetic CRS: WGS 84
joined_world$Extinct <- cut(joined_world$TotalSppExtBasin,
breaks = c(-Inf,c(0,1,2,3),Inf),
labels = c("0","1","2","3","15"),include.lowest=TRUE)
ggplot()+
theme_void()+
geom_sf(data=joined_world, aes(fill=Extinct), color=NA)+
scale_fill_manual(values=c("darkolivegreen3","gold2","orange3","red4"),
labels=c("1","2","3","15"))+ labs(fill="Total extinct species")threshold_value <- 2.5
filtered_data <- data_withNA %>% filter(dams_riverKM <= threshold_value)
library(dplyr)
different_obs <- anti_join(data_withNA, filtered_data, by = "hybas_id")
custom_colours <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a","yellow")
plot_ly(filtered_data, x = ~CSI_min, y = ~ThreatenedRatio_B, z = ~dams_riverKM,
color = ~continent, colors = custom_colours) %>%
add_markers(marker = list(size = 3))Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 3.1654 1.74110 1.67662 1.4792 1.42515 1.35322 1.30937
Proportion of Variance 0.2505 0.07579 0.07028 0.0547 0.05078 0.04578 0.04286
Cumulative Proportion 0.2505 0.32628 0.39656 0.4513 0.50204 0.54782 0.59068
PC8 PC9 PC10 PC11 PC12 PC13 PC14
Standard deviation 1.24077 1.1593 1.13093 1.0917 1.05912 1.03677 0.96699
Proportion of Variance 0.03849 0.0336 0.03198 0.0298 0.02804 0.02687 0.02338
Cumulative Proportion 0.62917 0.6628 0.69474 0.7245 0.75258 0.77945 0.80283
PC15 PC16 PC17 PC18 PC19 PC20 PC21
Standard deviation 0.95175 0.88344 0.83805 0.81661 0.7721 0.76822 0.61540
Proportion of Variance 0.02265 0.01951 0.01756 0.01667 0.0149 0.01475 0.00947
Cumulative Proportion 0.82547 0.84499 0.86254 0.87922 0.8941 0.90887 0.91834
PC22 PC23 PC24 PC25 PC26 PC27 PC28
Standard deviation 0.61292 0.56839 0.56071 0.53587 0.51636 0.48128 0.45242
Proportion of Variance 0.00939 0.00808 0.00786 0.00718 0.00667 0.00579 0.00512
Cumulative Proportion 0.92773 0.93581 0.94367 0.95085 0.95752 0.96331 0.96842
PC29 PC30 PC31 PC32 PC33 PC34 PC35
Standard deviation 0.42165 0.41572 0.40581 0.38230 0.3740 0.35599 0.31387
Proportion of Variance 0.00444 0.00432 0.00412 0.00365 0.0035 0.00317 0.00246
Cumulative Proportion 0.97287 0.97719 0.98131 0.98496 0.9885 0.99163 0.99409
PC36 PC37 PC38 PC39 PC40
Standard deviation 0.27995 0.24990 0.22596 0.1902 0.09194
Proportion of Variance 0.00196 0.00156 0.00128 0.0009 0.00021
Cumulative Proportion 0.99605 0.99761 0.99888 0.9998 1.00000
IncNodePurity
ire_pc_sse 2.0440932
crp_pc_sse 1.8930350
ppd_pk_sav 5.5427870
pop_ct_ssu 4.1981079
ire_pc_use 3.5280902
pst_pc_sse 4.2877228
ppd_pk_uav 6.3457655
pst_pc_use 4.5753773
ero_kh_sav 7.5318707
hft_ix_s09 4.2841666
hft_ix_s93 4.0061011
crp_pc_use 2.6675691
gdp_ud_ssu 2.9039621
ero_kh_uav 7.9507029
pop_ct_usu 3.7881073
hft_ix_u09 3.7826659
urb_pc_sse 0.4743752
gdp_ud_usu 2.6797934
hft_ix_u93 4.2279785
rdd_mk_sav 4.5319660
urb_pc_use 0.5134280
rdd_mk_uav 4.8897086
nli_ix_sav 5.4930418
rev_mc_usu 1.3949523
nli_ix_uav 2.4441400
sum_dams 0.5359298
dams_riverKM 0.7949689
dams_basinArea 0.7624746
riv_tc_ssu 4.9694795
riv_tc_usu 4.6713837
pac_pc_sse 2.8259419
sgr_dk_sav 9.5567625
pac_pc_use 2.9776126
CSI_max 0.1276659
for_pc_use 2.9268159
for_pc_sse 2.7028816
gdp_ud_sav 15.4595145
CSI_mainStem 4.9335657
CSI_min 4.9854327
CSI_wMean 4.1357270